Introduction to Reinforcement Learning

RPChe_
  2026-06-12 2026-06-12 创建   2026-08-03 02:07:44 2026-08-03 02:07:44 更新      114 字  1 分钟  

I've been interested in the topics in reinforcement learning. So here I'll briedly note Stanford CS234 Spring 2024.

The full text has been divided into multiple installments; this installment serves as the table of contents for the complete work.

Table of Contents

  1. Markov Decision Processes, Policy Evaluation, and RL Terminologies

    Markov Decision Process is the threotical foundation of reincement learning. This installment introduces the foundamental settings of Reinforcement Learning.

  2. Bellman Equations

    Bellman Equations are basic tools to characterize MDPs and MRPs. Here we aim to lay theoritcal foundations for general MDPs. While sometimes it's necessary and convenience to impose certain constraints, e.g. polish space or discrete space, to ensure regularity.

  • 标题: Introduction to Reinforcement Learning
  • 作者: RPChe_
  • 创建于 : 2026-06-12 00:00:00
  • 更新于 : 2026-08-03 02:07:44
  • 链接: https://rpche-6626.github.io/2026/06/12/RL/intro/
  • 版权声明: 本文章采用 CC BY-NC-SA 4.0 进行许可。
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目录
Introduction to Reinforcement Learning
  1. Table of Contents